QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES

International Journal in Foundations of Computer Science & Technology (IJFCST), Vol. 3, No.3, May 2013
DOI:10.5121/ijfcst.2013.3301 1
QOE MODEL FOR MULTIMEDIA CONTENT
DELIVERY FROM MCLOUD TO MOBILE DEVICES
Aleksandar Karadimce1
and Danco Davcev1
1
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius,
Skopje, R. Macedonia
akaradimce@ieee.org
danco.davcev@finki.ukim.mk
ABSTRACT
Integration of mobile devices in the m-learning systems provides the learner with scaffolding outside the
classroom, it allows them to easily store, record and deliver multimedia content in real-time. The process of
delivering multimedia learning objects (rich text, video, images, audio, animation, etc.) from the m-
learning systems to the users requires more computational resources than mobile devices can provide.
Considering the existence of different kinds of mobile browsers, which have limited support for HTML
plugins (Flash, Java and Silverlight), we face the challenge to deliver the adapted multimedia contents in
the interactive m-learning system. Installation of add-ons to the mobile browsers is just a provisional
solution that is not always possible on each mobile device. Generally, the m-learning systems presented on
the mobile device that not provide rich multimedia information leads to a degraded learning experience. In
order to provide users with multimedia content that is suitable for their mobile devices and according to
their needs we introduce the mobile cloud computing environment as paradigm that is ideal to overcome
these problems.
The proposed interactive mCloud system should provide high scale collaboration and interaction between
the professor and students, in direction of increasing the quality of learning. The main focus is the delivery
of multimedia learning objects to the users depending on the student’s cognitive style and adapting the
content in accordance with the context-aware network conditions. The main task within this paper is to
design a QoE model for estimating the multidimensional metric based on multimedia content adaptive
features.
KEYWORDS
Multimedia content, mobile cloud computing, user profile, QoE, QoL
1. INTRODUCTION
Throughout the traditional learning, educational process takes place in classroom, where
professors and students are meeting face to face at the same time and in the same place. During
the lectures professors present the learning material and interact with the students through
sequence of questions and answers, which allows them to give rough assessment on level of
achieved students’ knowledge. The learning materials are limited with certain multimedia objects
that the professors have previously arranged and it is not possible to adapt that content to
individual students learning requirements and cognitive style. Introduction of the mobile learning
systems have provided a potential for introducing anytime and anywhere educational environment
that supports the everyday student learning process and keep the continuity of life-long learning.
Mobile devices as service platforms for distance learning are being considered as excellent
educational tools for sharing multimedia learning content between learners and teachers.

2
Particular benefits of mobile learning systems are easy portability, real-time learning, autonomy,
interaction and collaboration in the process of m-learning [1]. However, mobile devices in order
to provide these advantages are facing certain limitation, such as computational capacity and
limited battery power, in the process of delivering secure multimedia based services to the
learners. Existing mobile learning systems, which are based on traditional teaching and
assessment principles, provide one and the same learning content to every student [2]. Certain
progress can be observed with the introduction of the interactive mobile live video learning
system in a cloud environment [3]. Here instructors video presentation was captured in private
cloud and later students using GPRS/WiFi connectivity on mobile device where able to
progressively download or play the video [3]. Existing m-learning environments still experience
diverse technological and Quality of Service (QoS) problems in the process of delivering different
kind of multimedia materials; adaptation of the learning material to individual student needs.
Another important challenge is the real-time interaction between student and the environment that
is difficult to achieve because of the context-aware bandwidth limitations.
The main advantages of the cloud computing in general are centralized storage, memory and data
processing providing increased capacity for multimedia distribution. The existing intelligent
multimedia delivery, based on cloud computing infrastructure called EVE (Elastic Video
Endpoint), provides dynamic provisioning of multimedia content [4]. Similarly, there is positive
improvement in m-learning systems with the emergence of EBTICs international iCampus
(intelligent campus) initiative, which is delivering customized and adapted learning to individuals
via mobile devices [6]. Its intelligent engine uses the Learning-Assessment-Communication-
Analysis (LACA) model. The dynamic engine generates content based on the assessment of
learners effectiveness and outcomes, rather than time spent on learning [6]. Benefit of delivery of
multimedia content in cloud computing for the m-learning has already provided enhanced
learning and more transparency, collaboration in the education [4], [7]. Important part of the
research has been dedicated to the development of the model for estimating the students
perception of Quality of Experience (QoE). This estimation model analyses the influence of
cognitive style perception of different type of multimedia content, taking into consideration the
multidimensional aspects of the QoE perception [8]. We have proposed conceptual QoE model
for multimedia mCloud learning environment that uses the benefits of the cloud computing
technology to deliver the learning materials.
This paper is organized as follows. Section II describes the proposed conceptual QoE model for
multimedia learning in cloud computing environment. Section III presents QoE as
multidimensional quality metrics. Section IV describes experienced quality evaluation and finally
Section V concludes the paper.
2. CONCEPTUAL QOE MODEL FOR MULTIMEDIA LEARNING IN CLOUD
COMPUTING ENVIRONMENT
Delivery of multimedia content is highly dynamic and innovative mobile technologies that are
introduced every day provide increased capabilities. When new media content is offered by the
teacher than it leads user’s preferences to change overtime, making a feedback loop. In order to
have an efficient learning system, which increases the Quality of Learning (QoL), it is necessary
continuously to receive feedback from the students in order to apply appropriate learning
techniques. This feedback loop in our proposed QoE model for multimedia learning should
provide collaboration between the participants using the social network interaction.
In order to increase the quality of delivering multimedia content current systems try to improve
the network parameters such as delay, jitter, packet loss, which are definitely important

3
parameters to be considered using the Quality of Service (QoS). However, they are mainly
measuring the accuracy of networked data delivery that is not sufficient to describe the actual
experience of the user. Quality of Experience means overall acceptability of an application or
service, as perceived subjectively by the end-user and represents multidimensional subjective
concept that is not easy to evaluate [9]. The relationship between QoE and QoS is non-trivial and
we have investigated and analysed what additional factors are influencing the user’s perception of
quality for delivery of multimedia content in cloud computing environment.
Taking in consideration and analysing all of the factors and parameters that affect the QoE, we
are proposing this conceptual model for immersive learning in cloud computing environment
(Figure 1). Input parameters in this model are network parameters, measured by the QoS and
the cognitive learning styles of the users, measured by the Quality of perception. Quality of
perception (QoP) is a novel term which includes not only users' enjoyment and satisfaction with a
multimedia presentation, but also their ability for content perception [10]. The proposed model
considers the user cognitive skills that are be used for modelling the knowledge and cognition in a
given domain, as a set of metrics connected to the appropriate education rules. The QoE model is
developed in order to integrate the learning process and user cognitive domain.
Figure 1. QoE model for multimedia learning in cloud computing environment
The Quality of Interaction (QoI) is defined as relation between User Interaction (UI), as a
feedback loop created by the Multimedia interaction (collaboration) model and the Domain
knowledge model, returned by the Quality of Feedback (QoF). The Quality of Interaction in the
m-learning has proven that is an important dimension in evaluating the quality of e-learning [11].
Perceived QoP and QoI by the end user are very important parameters that form the User
Experience (UX) model, measured by Quality of User Experience (QoUX). The UX model
deals with studying, designing and evaluating the experiences that people are experiencing
through the use of a system [12].
According to Mayer [5], the user experience is divided into four parts: perception measures,
rendering quality, physiological measures, and psychological measures. His research is limited
for cognitive style, learning preference, and cognitive ability for estimating the individual
differences along the visualizer–verbalizer dimension within a multimedia learning environment
[13]. This QoE model for multimedia learning is designed to integrate the user experience
(UX), user’s personal cognitive skills and technical (QoS) parameters together. In our
proposed model content adaptability will be performed by serving the user with preferable
multimedia content i.e. any combination of text, graphics, audio, video or animation that best
suites individual’s cognitive perception.

4
This way the Domain knowledge model will provide increased Quality of Learning (QoL) that
participates in delivering appropriate multimedia content (MM content) in the learning process.
3. QOE AS MULTIDIMENSIONAL QUALITY METRICS
Delivery of mobile multimedia services is difficult and challenging since in the mobile
environment; bandwidth and processing resources are limited. Audio-visual content is present in
on demand multimedia service that will meet user expectation of perceived audio visual quality,
which can differentiate the speech and non-speech contents. End-user quality expectation is
influenced by a number of factors including mutual compensation effects between audio and
video, content, encoding, and network settings as well as transmission conditions. Moreover,
audio and video are not only mixed in the multimedia stream, but there is even a synergy of
audio-visual media (audio and video).
QoE is a subjective metric that quantifies the perceived quality of a service by the viewers. As
such, QoE needs to correlate numerous parameters that affect the perceived quality such as the
encoding, transport, content, type of terminal, as well as the user’s expectations [14]. It defines
user expected quality and satisfaction from the multimedia learning content, efficient interaction
with the multimedia content provider, usability and effectiveness of the system as multimedia
content provider from the user point of view. The QoE is dynamic and adaptable measure is based
on a multidimensional concept that is affected by five major dimensions [8]:
- Effectiveness
- Usability
- Efficiency
- Expectations
- Context
Considering all of the above factors that the end user is expressing and the level of interaction
measured by the Quality of Interaction (QoI), we have proposed the dependence formula (1).
Based on the proposed conceptual model, on Figure 1, the QoI is defined as relation between the
User Interaction (UI), as a feedback loop created by the multimedia interaction (collaboration)
model and the domain knowledge model, returned by the Quality of Feedback (QoF), weighted
by the coefficient m (0<m<1). The Quality of Interaction in the e-learning has proven that is an
important dimension in evaluating the quality of e-learning systems [15].
)5]}......()([)({)(
)4)......(()(
)3]......()([)(
)2)......(()(
)1......()(
UIQoFmuQoPgeQoSfQoE
QoUXeQoSfQoE
UIQoFmuQoPgQoUX
QoIuQoPgQoUX
UIQoFmQoI





Quality of User Experience, given with formula (2), is influenced by level of user’s interaction
with mCloud, measured by QoI weighted by the coefficient u (0<u<1), and user’s content
perception QoP, weighted by the coefficient g (0<g<1). If we merge formula (1) and formula (2)
we will discover the 3 dimensional dependencies in UX model given with the formula (3).
From the QoE model for multimedia learning in cloud computing environment proposed in
Figure 1, we can conclude that QoE, formula (4), is related with network characteristics for

5
delivery of multimedia content measured by QoS, with functionally relation weight f(0<f<1), and
the degree of Quality of User Experience (QoUX), weighted by the coefficient e (0<e<1).
We can conclude form formula (5) that the QoE is multidimensional construct depending from 4
different characteristics: User Interaction (UI), Quality of Perception (QoP), Quality of Feedback
(QoF) and Quality of Service (QoS). This leads to conclusion that in order to measure the QoE,
users need to have efficient interaction level with the mCloud that will be achieved using the
social network communication, which will provide quality feedback and will provide estimate for
the perception level.
4. EXPERIENCED QUALITY EVALUATION
This paper focuses on the real-time interaction that provides collaborative and adaptive learning
environment for the students, which takes into account context-aware conditions and their
different cognitive perception for the multimedia content. Mayer [13] has described multimedia
learning as a field which has matured over the past decade, it proposes that m-learning courses
should be based on a cognitive theory. For the proposed model there exists one major open issue
how to measure the cognitive dimension to the individual student, in order to deliver multimedia
m-learning material which corresponds to the face-to-face knowledge communication. User
Interaction (UI) between the participants is achieved by using the social network interaction
forum. We propose to use questionnaires in order to estimate the cognitive dimension for the end
user, measured by Quality of Perception (QoP), we have adopted Learning Scenario
Questionnaire [13] for our research area of Database systems. Taking into consideration the
proposed QoE model for multimedia learning in mCloud environment and the QoE as
multidimensional construct given with formula (5) we have created the derived QoE dependence
model, given on Figure 2.
Figure 2. The derived QoE dependence model
The proposed QoE model for estimating the multidimensional metric has been demonstrated to
the students using the university distance learning web 2.0 mCloud portal. Students have been
enrolled in order to learn the course Database Systems, which covers the Entity Relations (ER)
and SQL query data manipulation class, see Figure 3.

6
Figure 3. The University web 2.0 mCloud learning portal
After completing the course Database Systems the group of 30 students, which participated in the
m-learning process, have been given Learning Scenario Questionnaire (LSQ), see Figure 4, in
order to conduct estimation of user’s preferences in five learning situations for database systems.
Figure 4. The Learning Scenario Questionnaire (LSQ)

7
Using the proposed QoE model for multimedia learning we received feedback from students via
the LSQ, which offers interactive collaboration between the participants. This experience quality
evaluation has been conducted using the course discussion forum, which allowed each user
independently to answer the questions from the Learning Scenario Questionnaire. The mCloud
learning portal is consisted of social network forum that provides interactive communication that
was used to conduct experience quality evaluation and receive feedback from the students, see
Figure 5.
Figure 5. The University web 2.0 mCloud social network forum
After analysis of these results we have gained one more metric, the Quality of Feedback (QoF) in
our proposed conceptual QoE model for multimedia mCloud learning environment. Complete
review of the results from the completed Learning Scenario Questionnaire is given in Figure 6.
Figure 6. The derived QoE dependence model
These results confirm that majority of the students are video learners – verbalizers, they prefer to
receive graphical/image learning objects. The peak is emphasized in question B from the
Learning Scenario Questionnaire that highlights the use of labelled diagram for on the question
“how executing SQL query works”. However, in the question C from the Learning Scenario

8
Questionnaire the audio learners – verbalizers prefer to receive textual instructions in form of
learning objects, have shown increased interest. This QoE model for estimating the
multidimensional metric that is proposed in this research evidently provides increased student
satisfaction and advantage of the used methodology of learning.
5. CONCLUSIONS
The proposed QoE model for multimedia mCloud learning environment uses the benefits of the
cloud computing technology to deliver the learning materials. It enables the students to learn their
topics of interest transparently and immediately using various devices whenever and wherever
they want. In this paper we have proposed a QoE model for estimating the multidimensional
metric based on audio and video multimedia content adaptive features. Results from the research
have concluded that the QoE metrics as multidimensional construct is dependent from 4 different
characteristics: User Interaction (UI), Quality of Perception (QoP), Quality of Feedback (QoF)
and Quality of Service (QoS). These factors directly influence the user satisfaction with the
system used, resulting to increased knowledge that has impact to the learning process in general.
We can conclude that cognitive style and user interaction, with hands on practice, provides
increased educational benefit, measured by increased Quality of Learning (QoL).
REFERENCES
[1] Chen, S., Lin, M., Zhang, H. (2011) “Research of mobile learning system based on cloud computing”,
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[2] Hirsch, B., Ng, J. W.P. (2011) “Education Beyond the Cloud: Anytime-anywhere learning in a smart
campus environment”, Proceedings of the 6th International Conference of Internet Technology and
Secured Transactions, 11-14 Dec. 2011, Abu Dhabi, Unated Arab Emirates. pp. 718-723. ISBN:
9781457708848.
[3] Saranya, M. and Vijayalakshmi, M. (2011) “Interactive mobile live video learning system in cloud
environment”, Proceedings of IEEE-International Conference on Recent Trends in Information
Technology, ICRTIT, June 2011. pp. 673- 677. ISBN: 978-1-4577-0588-5.
[4] Blair, A. et al. (2012) “Cloud based Dynamically Provisioned Multimedia Delivery: An Elastic Video
Endpoint”, Proceedings of The Third International Conference on Cloud Computing, GRIDs, and
Virtualization, IARIA, CLOUD COMPUTING 2012, 22-27 Jul. 2012. Nice, France. pp. 260-265.
ISBN: 978-1-61208-216-5.
[5] Mayer, R. E. (2001) Multimedia learning. New York: Cambridge University Press
[6] Wang, M., Ng, J. W.P. (2012) “Intelligent Mobile Cloud Education. Smart anytime-anywhere
learning for the next generation campus environment”, Proceedings of Eighth International
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[7] Alabbadi, M. M. (2011) “Mobile learning (mLearning) based on cloud computing: mLearning as a
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[8] De Moor, K. and De Marez., L. (2008) “The Challenge of User- and QoE-centric research and
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[9] Agboma, F. and Liotta. A. (2010) “Quality of experience management in mobile content delivery
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[12] Roto, V., Law, E., Vermeeren, A., and Hoonhout, J. (2010) “User Experience” White Paper. Outcome
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Authors
Aleksandar Karadimce is currently working in the University of Information
Science and Technology "St. Paul The Apostle" in Ohrid as a Research and
Teaching Assistant for Information Science and Technology. In the same time I'm
PhD candidate at the Faculty of Computer Science and Engineering University Ss
Cyril and Methodius in Skopje on Computer Science and Engineering
department. I have previous working experience in two Specialized
Telecommunication Companies in Macedonia. His research interests lie in the
areas of computer science and engineering; mobile network communications;
Cloud computing; Mobile computing; Multimedia Databases and Educational
Systems; Quality of Experience in delivery of multimedia content. He is a
member of Institute of Electrical and Electronics Engineers (IEEE) and IEEE
Technical Societies: IEEE Computer Society Membership.
Danco Davcev currently is a professor in Computer Science and Engineering at
the University "Sts. Cyril & Methodius", Skopje (Macedonia). He has more than
250 research papers presented on International Conferences or published in
International Journals in Computer Science and Informatics. He has received his
"Doctor – Ingenieur" degree in Computer Science from the University of ORSAY
(Paris, France) in 1975 and Ph.D. degree in Informatics from the University of
Belgrade (YU) in 1981. From 1984 - 1985 he was a Fulbright postdoctoral
research scientist at the University of California, San Diego (USA). His research
interests include Multimedia Databases and Educational Systems, Spatio-
temporal data and 3D objects, Intelligent Information Systems and data fusion,
Distributed Systems; Cloud computing, Wireless sensor systems, mobile computing etc. He is a senior
member of ACM and a Senior Member of IEEE.

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QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES